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Bioinformatics Vol. 17 no. 10 2001
Pages 942-948
© 2001 Oxford University Press

Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm

R. R. Mallios

Office of Sponsored Projects and Research, University of California, San Francisco, 2615 East Clinton Avenue, Fresno, CA 93703, USA

Received on April 20, 2001 ; revised on July 12, 2001 ; accepted on July 12, 2001

Motivation: Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design.

Results: An iterative stepwise discriminant analysis meta-algorithm is utilized to derive a quantitative motif for classifying potential agretopes as high-, moderate- or non-binders for HLA-DR1, a class II MHC molecule. A large molecular online database provides the input for this data-driven algorithm. The model correctly classifies over 85% of the peptides in the database.

Availability: Stepwise discriminant analysis software is available commercially in SPSS and BMDP statistical software packages. Peptides known to bind MHC molecules can be downloaded from http://wehih.wehi.edu.au/mhcpep/. Peptides known not to bind HLA-DR1 are available from the author upon request.

Contact: ronna{at}ucsfresno.edu


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